How we build.
The difference between a demo and a system is what happens at the edges. These are the principles we apply to every engagement.
Reliability patterns from firmware
01Half of our practice comes from electronics, firmware, and financial markets infrastructure. State machines, circuit breakers, watchdog timers, and checkpointing aren't AI buzzwords here — they're patterns from systems that had to keep running. AI systems need the same discipline.
Evaluation-first design
02If you can't measure it, you can't ship it. Every system begins with the eval that defines success — golden sets, regression gates, production scoring. Demos pass once; evaluations pass forever.
Failure-aware AI
03Hallucinations, drift, prompt injection, and edge cases are design constraints, not surprises. Guardrails belong in the architecture, not bolted on after a postmortem.
Retrieval quality > model size
04Hybrid search, re-ranking, and good chunking beat bigger models in production. Most wins come from the data pipeline — BM25 plus pgvector, reciprocal rank fusion, cross-encoder reranking — not the parameter count.
Compound reliability
05A 95% step in a 5-step pipeline is 77% end-to-end. We design with the chain in mind, not the demo. Every component carries its own error budget.
Human in the loop, by design
06Confidence thresholds, review queues, and override paths are part of the product — not an admin afterthought. Trust is built by what the system refuses to do.